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@Techmeme - 46d
OpenAI has released its new O3 model which demonstrates significantly improved performance in reasoning, coding, and mathematical problem-solving compared to its previous models. The O3 model achieves 75.7% on the ARC Prize Semi-Private Evaluation in low-compute mode and an impressive 87.5% in high-compute mode. However, this performance comes at a very high cost, with the top-end system costing around $10,000 per task which makes it very expensive to run.
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@Techmeme - 46d
OpenAI's new o3 model has achieved a significant breakthrough on the ARC-AGI benchmark, demonstrating advanced reasoning capabilities through a 'private chain of thought' mechanism. This approach involves the model searching over natural language programs to solve tasks, with a substantial increase in compute leading to a vastly improved score of 75.7% on the Semi-Private Evaluation set within a $10k compute limit, and 87.5% in a high-compute configuration. The o3 model uses deep learning to guide program search, moving beyond basic next-token prediction. Its ability to recombine knowledge at test time through program execution marks a major step toward more general AI capabilities.
The o3 model's architecture and performance represents a form of deep learning-guided program search, where it explores many paths through program space. This process, which can involve tens of millions of tokens and cost thousands of dollars for a single task, is guided by a base LLM. While o3 appears to be more than just next-token prediction, it’s still being speculated what the core mechanisms of this process are. This breakthrough highlights how increases in compute can drastically improve performance and marks a substantial leap in AI capabilities, moving far beyond previous GPT model performance. The model's development and testing also revealed that it cost around $6,677 to run o3 in "high efficiency" mode against the 400 public ARC-AGI puzzles for a score of 82.8%. Recommended read:
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@manlius.substack.com - 68d
This year's Nobel Prize in Physics has been awarded to John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto. The Royal Swedish Academy of Sciences recognized their "foundational discoveries and inventions that enable machine learning with artificial neural networks." Hopfield's research centered on associative memory using Hopfield networks, while Hinton's contributions focused on methods for autonomously identifying patterns within data, utilizing Boltzmann machines. Their work is considered groundbreaking in the field of artificial intelligence and has implications for various areas of physics, including the creation of novel materials.
The award has sparked debate within the physics community, with some questioning the appropriateness of awarding a Physics Nobel for work primarily in computer science. While Hopfield's background is in condensed matter physics and his work draws inspiration from concepts like spin glass theory, Hinton's background is in artificial intelligence. The choice reflects the increasing interconnectedness and influence of computer science on other scientific fields, pushing the boundaries of traditional disciplinary lines. Despite the controversy, the Nobel committee has underscored the fundamental contributions of Hopfield and Hinton. Their innovative work on artificial neural networks, drawing upon and extending principles of statistical physics, has revolutionized machine learning, creating significant advancements with far-reaching applications beyond the realm of physics. The prize is a testament to the groundbreaking nature of their research and its transformative impact on multiple scientific and technological areas. Recommended read:
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@medium.com - 55d
Recent articles and blog posts have highlighted key machine learning concepts and their applications, focusing particularly on the role of probability and statistics. These foundational mathematical tools are essential for understanding how machine learning models make decisions. Key areas explored include probability distributions like uniform, normal, Bernoulli, binomial, and Poisson, each with specific applications in model training and data analysis. Furthermore, the concept of conditional probability is discussed, explaining how the likelihood of an event changes based on other events, using real-world examples. Understanding of these concepts is fundamental for building effective ML models.
The importance of data sampling in machine learning has also been addressed, emphasizing how crucial representative data sets are to achieving accurate predictions. Techniques such as random sampling and stratified sampling ensure models can generalize well to new data, addressing potential biases caused by class imbalances through over-sampling or under-sampling techniques. Articles also showcase how techniques like decision trees and random forests are being applied for tasks such as customer churn prediction, and the use of matrices and GPUs for accelerating deep learning computations. The interrelationship between math and coding is highlighted, noting the significance of mathematical principles in algorithms, data structures, and computational complexity. Recommended read:
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@the-decoder.com - 22d
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pub.towardsai.net
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AI research is rapidly advancing, with new tools and techniques emerging regularly. Johns Hopkins University and AMD have introduced 'Agent Laboratory', an open-source framework designed to accelerate scientific research by enabling AI agents to collaborate in a virtual lab setting. These agents can automate tasks from literature review to report generation, allowing researchers to focus more on creative ideation. The system uses specialized tools, including mle-solver and paper-solver, to streamline the research process. This approach aims to make research more efficient by pairing human researchers with AI-powered workflows.
Carnegie Mellon University and Meta have unveiled a new method called Content-Adaptive Tokenization (CAT) for image processing. This technique dynamically adjusts token count based on image complexity, offering flexible compression levels like 8x, 16x, or 32x. CAT aims to address the limitations of static compression ratios, which can lead to information loss in complex images or wasted computational resources in simpler ones. By analyzing content complexity, CAT enables large language models to adaptively represent images, leading to better performance in downstream tasks. Recommended read:
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@medium.com - 19d
Recent publications have highlighted the importance of statistical and probability concepts, with an increase in educational material for data professionals. This surge in resources suggests a growing recognition that understanding these topics is crucial for advancing AI and machine learning capabilities within the community. Articles range from introductory guides to more advanced discussions, including the power of continuous random variables and the intuition behind Jensen's Inequality. These publications serve as a valuable resource for those looking to enhance their analytical skillsets.
The available content covers a range of subjects including binomial and Poisson distributions, and the distinction between discrete and continuous variables. Practical applications are demonstrated using tools like Excel to predict sales success and Python to implement uniform and normal distributions. Various articles also address common statistical pitfalls and strategies to avoid them including skewness and misinterpreting correlation. This shows a comprehensive effort to ensure a deeper understanding of data-driven decision making within the industry. Recommended read:
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@vatsalkumar.medium.com - 13d
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medium.com
Recent articles have focused on the practical applications of random variables in both statistics and machine learning. One key area of interest is the use of continuous random variables, which unlike discrete variables can take on any value within a specified interval. These variables are essential when measuring things like time, height, or weight, where values exist on a continuous spectrum, rather than being limited to distinct, countable values. The concept of the probability density function (PDF) helps us to understand the relative likelihood of a variable taking on a particular value within its range.
Another significant tool being explored is the binomial distribution, which can be applied using programs like Microsoft Excel to predict sales success. This distribution is suited to situations where each trial has only two outcomes – success or failure, like a sales call resulting in a deal or not. Using Excel, one can calculate the probability of various sales outcomes based on factors like the number of calls made and the historical success rate, aiding in setting achievable sales goals and comparing performance over time. Also, the differentiation between binomial and poisson distribution is critical for correct data modelling, with binomial experiments requiring fixed number of trials and two outcomes, unlike poisson. Finally, in the world of random variables, a sequence of them conditionally converging to a constant value has been discussed, highlighting that if the sequence converges, knowing it passes through some point doesn't change the final outcome. Recommended read:
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@quantumcomputingreport.com - 34d
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medium.com
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Quantum computing is rapidly advancing with significant implications for various fields, particularly in the areas of randomness and security. Researchers are exploring the use of quantum computing to redefine randomness and enhance machine learning through technologies such as Quantum Support Vector Machines. These advancements highlight the technology's potential to revolutionize data analysis and processing. Simultaneously, there is a growing focus on developing quantum-resistant encryption methods to protect internet security from future quantum computer attacks. This is vital, as traditional encryption methods could become vulnerable to the power of quantum computing.
The pursuit of robust quantum encryption is evident in recent developments, including the work of cryptographers designing encryption methods that are invulnerable to quantum computers. Additionally, Russia has unveiled a 50-qubit quantum computer prototype, signaling a major step in their quantum computing roadmap and a move towards increased quantum capabilities. Furthermore, institutions like IonQ and Oak Ridge National Laboratory are working on noise-tolerant quantum computing techniques, advancing the technology towards practical applications and commercial availability. These advances all underscore quantum computing's increasing importance as a pivotal technology for the future. Recommended read:
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